def generate_image(description: str) -> ImageOutput:
    image, _ = opper.call(
        name="generate_image",
        output_type=ImageOutput,
        input=description,
        model="gcp/imagen-3.0-generate-001-eu",
        configuration=CallConfiguration(
            model_parameters={
                "aspectRatio": "9:16",
            }
        ),
    ) 
    return image


description = "portrait of a person standing in front of a park. vibrant, autumn colors"

path = save_file(generate_image(description).bytes)
print(path)

async function testImageGeneration() {
    const image = await client.generateImage({
        model: "gcp/imagen-3.0-generate-001-eu",
        prompt: "portrait of a person standing in front of a park. vibrant, autumn colors",
        parameters: {
            aspectRatio: "9:16",
        }
    });

    const tempFilePath = path.join(os.tmpdir(), "image.png");
    fs.writeFileSync(tempFilePath, image.bytes);
    console.log(`image written to temporary file: ${tempFilePath}`);
}

testImageGeneration();

# special type for images, this is to capture the need for encoding the image in the right format
from opperai import ImageInput 

description, response = await aopper.call(
    name="async_describe_image",
    instructions="Create a short description of the image",
    output_type=Description,
    input=Image(
        image=ImageInput.from_path("examples/cat.png"),
    ),
    model="openai/gpt-4o",
)

// special function to read images, this is to capture the need for encoding the image in the right format
import { opperImage } from "opperai"; 

const { message } = await client.call({
    parent_span_uuid: trace.uuid,
    name: "node-sdk/call/multimodal/image-input",
    instructions: "Create a short description of the image",
    input: {image: image("examples/cat.png")},
    model: "openai/gpt-4o",
});

from opperai import ImageOutput

cat, _ = await aopper.call(
    name="generate_cat",
    output_type=ImageOutput,
    input="Create an image of a cat",
)

const cat = await client.generateImage({
    parent_span_uuid: trace.uuid,
    prompt: "Create an image of a cat",
});

output, _ = opper.call(
    name="changelog/python/call-with-examples",
    instructions="extract the weekday from a text",
    examples=[
        Example(input="Today is Monday", output="Monday"),
        Example(input="Friday is the best day of the week", output="Friday"),
        Example(
            input="Saturday is the second best day of the week", output="Saturday"
        ),
    ],
    input="Wonder what day it is on Sunday",
)

span = opper.traces.start_trace(name="my_function", input="Hello, world!")
# business logic here
span.end()

with opper.traces.start(name="my_function", input="Hello, world!") as span:
    # business logic here

@trace
def my_function(input: str) -> str:
    # business logic here

opper.call(name="anthropic/claude-3-haiku", input="Hello, world!")

// Start parent trace
const trace = await client.traces.start({
    name: "node-sdk/tracing-manual",
    input: "Trace initialization",
});

// You can optionally start a child span and provide the input
const span = await trace.startSpan({
    name: "node-sdk/tracing-manual/span",
    input: "Some input given to the span",
});

// A metric and/or comment can be saved to the span
// A span generation can also be saved using .saveGeneration()
await span.saveMetric({
    dimension: "accuracy",
    score: 1,
    comment: "This is a comment",
});

// End the span and provide the output
await span.end({
    output: JSON.stringify({ foo: "bar" }),
});

// End the parent trace
await trace.end({ output: JSON.stringify({ foo: "bar" }) });

const { message } = await client.call({
    name: "node-sdk/call/basic",
    input: "what is the capital of sweden",
});

def run():
    opper = Opper()
    spans = opper.spans

    with spans.start("transform", input="Hello, world!") as span:
        t0 = datetime.now(timezone.utc)
        manually_call_llm()
        t1 = datetime.now(timezone.utc)
        span.save_generation(
            called_at=t0,
            duration_ms=int((t1 - t0).total_seconds() * 1000),
            response="I'm happy because I'm happy",
            model="anthropic/claude-3-haiku",
            messages=[
                {
                    "role": "user",
                    "content": "Hello, world!",
                }
            ],
            cost=3.1,
            prompt_tokens=10,
            completion_tokens=10,
            total_tokens=20,
        )